Systems and methods for aircraft power management and distribution

ABSTRACT

A system for aircraft power management and distribution, including a sensor suite configured to measure battery pack data. The system includes a battery pack with a plurality of batteries and a battery monitoring component. This battery monitoring component is configured to measure battery pack data. The system also has electric power converters, each connected to a battery of the plurality of batteries. The system also includes a controller configured to control each electric power converter; receive an estimated charge from each battery; select and enable electric power converters based on the estimated charge; compare the total output of the enabled electric power converters against an optimal operating region; and adjust the number of the one or more enabled electric power converters accordingly.

FIELD OF THE INVENTION

The present invention generally relates to the field of powermanagement. In particular, the present invention is directed to powermanagement and distribution in an electric aircraft.

BACKGROUND

Various components, such as computer systems, on board an electricaircraft often need a to be supplied with a low voltage. Therefore, itis sometimes necessary to covert the high voltage output of the on-boardbatteries to low voltage in order to supply those components with power.Additionally, these power converters only operate at peak efficiencywhen the output current is within a certain window. Existing solutionsto not provide an adequate way of managing on-board batteries tomaximize the efficiency of the power converters.

SUMMARY OF THE DISCLOSURE

In an aspect, the present disclosure is directed to a system including asensor suite configured to measure battery pack data. The systemincludes a battery pack, the battery pack including a plurality ofbatteries electrically connected together in a series, batterymonitoring component, the battery monitoring component configured tomeasure battery pack data, and a plurality of electric power converters,wherein each electric power converter of the plurality of electric powerconverters is connected to a battery in the plurality of batteries. Thesystem also includes an electric power converter controller, theelectric power converter controller configured to control each electricpower converter of a plurality of electric power converters; select,from the plurality of electric power converters, one or more electricpower converters to enable; enable the one of more selected electricpower converters, wherein an enabled electric power converter convertsan input at a first voltage level to an output at a second voltage leveland the outputs of the one or more enabled electric power converters arecombined to create a total output; compare the total output of the oneor more enabled electric power converters against an optimal operatingregion, having a first threshold value and a second threshold value; andadjust, when the total output of the one or more enabled electric powerconverters is outside of the optimal operating region, a number of theone or more enabled electric power converters, wherein adjusting furthercomprises adjusting as a function of the battery pack data.

In another aspect, the present disclosure is directed to a methodincluding measuring battery pack data for a plurality of batteries in abattery pack. The method further including selecting, from a pluralityof electric power converters, wherein each electric power converter inthe plurality of electric power converters is connected to a battery ofthe plurality of batteries, one or more electric power converters toenable. The method includes enabling the one of more selected electricpower converters, comprising converting, using an electric powerconverter of the one or more selected electric power converters, aninput at a first voltage level to an output at a second voltage level,and combining each output of the one or more enabled electric powerconverters to create a total output. The method includes comparing thetotal output of the one or more enabled electric power convertersagainst an optimal operating region, having a first threshold value anda second threshold value. The method also includes adjusting, when thetotal output of the one or more enabled electric power converters isoutside of the optimal operating region, a number of the one or moreenabled electric power converters.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an embodiment of a power distributionsystem.

FIG. 2 is a block diagram of an exemplary battery management component.

FIG. 3 is a diagram of a sensor suite.

FIG. 4 is a block diagram of an embodiment of a battery monitoring andmanagement system.

FIG. 5 is a graph of the loading vs. efficiency for an electric powerconverter.

FIG. 6 is a diagrammatic representation of a method for aircraft powermanagement and distribution.

FIG. 7 is a block diagram of an exemplary flight controller.

FIG. 8 is a block diagram of a machine-learning module that may performone or more machine-learning processes as described in this disclosure.FIG. 9 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for aircraft power management and distribution. Inan embodiment, a sensor suite may measure battery pack data, the batterypack including a plurality of batteries in series, a battery monitoringcomponent, a battery management component, and a plurality of electricpower converters each connected to a battery of the plurality ofbatteries. This embodiment also includes an electric power convertercontroller that can enable and disable electric power converters basedon an estimated charge state for the associated battery or batteries.

Aspects of the present disclosure can be used to manage electric powerconverters within a battery pack on an electric aircraft to ensure thatthey are operating efficiently as well as to monitor the other on-boardbatteries for other problems.

Aspects of the present disclosure allow the management and monitoring ofan on-board battery pack with associated electric power converters.Exemplary embodiments illustrating aspects of the present disclosure aredescribed below in the context of several specific examples.

Referring to FIG. 1, FIG. 1 depicts an embodiment of power distributionsystem 100. In this embodiment, power distribution system 100 isdepicted as including two battery systems 104A and 104B. However, inother embodiments, there may be any number of battery systems. Forinstance, in some embodiments, there may be 4 battery systems. In otherembodiments there may be more than 4 battery systems; in yet otherembodiments, there may be less than four battery systems. Batterysystems 104A and 104B are electronically connected together in series.For the purposes of this disclosure, electronically connected means anyconnection sufficient to allow the conduction of electricity between theparts being connected.

With continued reference to FIG. 1, each battery system 104 contains abattery 108 and an electric power converter 116. Batteries 108 mayinclude any type of energy source where chemical energy is convertedinto electrical energy. As a non-limiting example, batteries 108 couldbe lithium-ion batteries. Battery systems 104 may be enabled or disabledby the controller 112 depending on the power required by the on-boardsystems. In an embodiment, each of the batteries 108 may be electricallyconnected to its own electric power converter 116. In other embodiments,more than one battery 108 may be electronically connected in series andthen connected to an electric power converter 116. Electric powerconverters 116 receive the voltage output of batteries 108 and convertthat voltage to a low voltage (V Low) 120. The low voltage 120 isvoltage required by the on-board systems. In some embodiments, the lowvoltage produced when all of the battery systems 104 are enabled may bethe peak voltage required by the on-board systems. Thus, when one ormore of the battery systems 104 are disabled, then low voltage 120 willbe less than the peak voltage required by the on-board systems.

With continued reference to FIG. 1, power distribution system 100contains controller 112. Controller 112 includes an electric powerconverter controller 124, and battery monitoring component 128. Powerdistribution system 100 may also contain battery management component132.

With continued reference to FIG. 1, in some other embodiments, electricpower converter controller 124 may use other criteria for selectivelyenabling and disabling electric power converters 116. In one embodiment,electric power converter controller 124 may receive a state of chargefor each battery system 104, in another embodiment, electric powerconverter controller 124 may estimate the state of charge for eachbattery system 104 based on the current output of electric powerconverters 116 a-b. In another embodiment, electric power convertercontroller 124 may estimate the length of time that each of electricpower converters 116 a-b based on the current output of each of electricpower converters 116 a-b. In yet another embodiment; electric powerconverter controller 124 may rotate through each of the electric powerconverters 116 a-b, ensuring that each electric power converter 116 a-bis enabled for the same amount of time. Electric power convertercontroller 124 may then use this information to decide which electricpower converters 116 should be enabled or disabled in order to providethe desired low voltage 120.

Referring now to FIG. 2, an embodiment of battery management component200 is presented as a block diagram. In some embodiments, batterymanagement component 200 may be an embodiment of battery managementcomponent 132 in FIG. 1. Battery management component 200 may beintegrated in a battery pack configured for use in an electric aircraft.Battery management component 200 includes first battery managementsub-component 204 disposed on a first end of the battery pack. One ofordinary skill in the art will appreciate that there are various areasin and on a battery pack and/or subassemblies thereof that may includefirst battery management sub-component 204. First battery managementsub-component 204 may take any suitable form. In a non-limitingembodiment, first battery management sub-component 204 may include acircuit board, such as a printed circuit board and/or integrated circuitboard, a subassembly mechanically coupled to at least a portion of thebattery pack, standalone components communicatively coupled together, oranother undisclosed arrangement of components; for instance, and withoutlimitation, a number of components of first battery managementsub-component 204 may be soldered or otherwise electrically connected toa circuit board. First battery management sub-component 204 may bedisposed directly over, adjacent to, facing, and/or near a batterymodule and specifically at least a portion of a battery cell. Firstbattery management sub-component 204 includes first sensor suite 212.First sensor suite 212 is configured to measure, detect, sense, andtransmit a first plurality of battery pack data 216 to data storagesystem 220. Referring again to FIG. 2, battery management component 200includes first battery management sub-component 204. First batterymanagement sub-component 204 is disposed in or on a second end ofbattery pack 224. First battery management sub-component 204 may includesecond sensor suite 228. Second sensor suite 228 may be consistent withthe description of any sensor suite disclosed herein. Second sensorsuite 228 is configured to measure second plurality of battery pack data232. Second plurality of battery pack data 232 may be consistent withthe description of any battery pack data disclosed herein. Secondplurality of battery pack data 232 may additionally or alternativelyinclude data not measured or recorded in another section of batterymanagement component 200. Second plurality of battery pack data 232 maybe communicated to additional or alternate systems to which it iscommunicatively coupled. Second sensor suite 228 includes a humiditysensor consistent with any humidity sensor disclosed herein, namelyhumidity sensor 204. Battery management component and the first andsecond sensor suite may be consistent with the battery management systemand sensor suites, respectively, disclosed in U.S. patent applicationSer. No. 17/108,798, filed on Dec. 1, 2020, entitled “Systems andMethods for a Battery Management System Integrated in a Battery PackConfigured for Use in an Electric Aircraft” and incorporated hereby byreference in its entirety.

With continued reference to FIG. 2, first battery managementsub-component 204 disposed in or on battery pack 224 may be physicallyisolated from first battery management sub-component 204 also disposedon or in battery pack 224. “Physical isolation”, for the purposes ofthis disclosure, refer to a first system's components, communicativecoupling, and any other constituent parts, whether software or hardware,are separated from a second system's components, communicative coupling,and any other constituent parts, whether software or hardware,respectively. First battery management sub-component 204 and firstbattery management sub-component 208 may perform the same or differentfunctions in battery management component 200. In a non-limitingembodiment, the first and second battery management components performthe same, and therefore redundant functions. If, for example, firstbattery management sub-component 204 malfunctions, in whole or in part,first battery management sub-component 208 may still be operatingproperly and therefore battery management component 200 may stilloperate and function properly for electric aircraft in which it isinstalled. Additionally, or alternatively, first battery managementsub-component 208 may power on while first battery managementsub-component 204 is malfunctioning. One of ordinary skill in the artwould understand that the terms “first” and “second” do not refer toeither “battery management components” as primary or secondary. Innon-limiting embodiments, first battery management sub-component 204 andfirst battery management sub-component 208 may be powered on and operatethrough the same ground operations of an electric aircraft and throughthe same flight envelope of an electric aircraft. This does not precludeone battery management component, first battery management sub-component204, from taking over for first battery management sub-component 208 ifit were to malfunction. In non-limiting embodiments, the first andsecond battery management components, due to their physical isolation,may be configured to withstand malfunctions or failures in the othersystem and survive and operate. Provisions may be made to shield firstbattery management sub-component 204 from first battery managementsub-component 208 other than physical location such as structures andcircuit fuses. In non-limiting embodiments, first battery managementsub-component 204, first battery management sub-component 208, orsubcomponents thereof may be disposed on an internal component or set ofcomponents within battery pack 224.

Referring again to FIG. 2, first battery management sub-component 204may be electrically isolated from first battery management sub-component208. “Electrical isolation”, for the purposes of this disclosure, referto a first system's separation of components carrying electrical signalsor electrical energy from a second system's components. First batterymanagement sub-component 204 may suffer an electrical catastrophe,rendering it inoperable, and due to electrical isolation, first batterymanagement sub-component 208 may still continue to operate and functionnormally, managing the battery pack of an electric aircraft. Shieldingsuch as structural components, material selection, a combinationthereof, or another undisclosed method of electrical isolation andinsulation may be used, in non-limiting embodiments. For example, arubber or other electrically insulating material component may bedisposed between the electrical components of the first and secondbattery management components preventing electrical energy to beconducted through it, isolating the first and second battery managementcomponents from each other.

With continued reference to FIG. 2, battery management component 200includes data storage system 220. Data storage system 220 is configuredto store first plurality of battery pack data 216 and second pluralityof battery pack data 232. Data storage system 220 may include adatabase. Data storage system 220 may include a solid-state memory ortape hard drive. Data storage system 220 may be communicatively coupledto first battery management sub-component 204 and first batterymanagement sub-component 204 and may be configured to receive electricalsignals related to physical or electrical phenomenon measured and storethose electrical signals as first battery pack data 216 and secondbattery pack data 232, respectively. Alternatively, data storage system220 may include more than one discrete data storage systems that arephysically and electrically isolated from each other. In thisnon-limiting embodiment, each of first battery management sub-component204 and first battery management sub-component 204 may store firstbattery pack data 216 and second battery pack data 232 separately. Oneof ordinary skill in the art would understand the virtually limitlessarrangements of data stores with which battery management component 200could employ to store the first and second plurality of battery packdata.

Referring again to FIG. 2, data storage system 220 stores firstplurality of battery pack data 216 and second plurality of battery packdata 232. First plurality of battery pack data 216 and second pluralityof battery pack data 232 may include total flight hours that batterypack 224 and/or electric aircraft have been operating. The first andsecond plurality of battery pack data may include total energy flowedthrough battery pack 224. Data storage system 220 may be communicativelycoupled to sensors that detect, measure and store energy in a pluralityof measurements which may include current, voltage, resistance,impedance, coulombs, watts, temperature, or a combination thereof.Additionally, or alternatively, data storage system 220 may becommunicatively coupled to a sensor suite consistent with thisdisclosure to measure physical and/or electrical characteristics. Datastorage system 220 may be configured to store first battery pack data216 and second battery pack data 232 wherein at least a portion of thedata includes battery pack maintenance history. Battery pack maintenancehistory may include mechanical failures and technician resolutionsthereof, electrical failures and technician resolutions thereof.Additionally, battery pack maintenance history may include componentfailures such that the overall system still functions. Data storagesystem 220 may store the first and second battery pack data thatincludes an upper voltage threshold and lower voltage thresholdconsistent with this disclosure. First battery pack data 216 and secondbattery pack data 232 may include a moisture level threshold. Themoisture level threshold may include an absolute, relative, and/orspecific moisture level threshold. Battery management component 200 maybe designed to the Federal Aviation Administration (FAA)'s DesignAssurance Level A (DAL-A), using redundant DAL-B subsystems.

Referring now to FIG. 3, an embodiment of sensor suite 300 is presented.Sensor suite 300 may include a plurality of independent sensors, asdescribed herein, where any number of the described sensors may be usedto measure any number of physical or electrical quantities associatedwith an aircraft power system or an electrical energy storage system. Insome other embodiments, sensor suite 300 may comprise only a singlesensor or in other embodiments, it may include a plurality of sensors ofa single sensor type. Independent sensors may include separate sensorsmeasuring physical or electrical quantities that may be powered byand/or in communication with circuits independently, where each may sendsensor output to a control circuit such as a user graphical interface.In a non-limiting example, there may be four independent sensors housedin and/or on battery pack 224 measuring temperature, electricalcharacteristic such as voltage, amperage, resistance, or impedance, orany other parameters and/or quantities as described in this disclosure.In an embodiment, use of a plurality of independent sensors may resultin redundancy configured to employ more than one sensor that measuresthe same phenomenon, those sensors being of the same type, a combinationof, or another type of sensor not disclosed, so that in the event onesensor fails, the ability of battery management component 200 and/oruser to detect phenomenon is maintained and in a non-limiting example, auser alter aircraft usage pursuant to sensor readings.

With continued reference to FIG. 3, Sensor suite 300 may be suitable foruse as first sensor suite 212 and/or second sensor suite 228 asdisclosed with reference to FIG. 2 hereinabove. Sensor suite 300 mayinclude, in a non-limiting example, a humidity sensor 304. Humidity, asused in this disclosure, is the property of a gaseous medium (almostalways air) to hold water in the form of vapor. An amount of water vaporcontained within a parcel of air can vary significantly. Water vapor isgenerally invisible to the human eye and may be damaging to electricalcomponents. There are three primary measurements of humidity, absolute,relative, specific humidity. “Absolute humidity,” for the purposes ofthis disclosure, describes the water content of air and is expressed ineither grams per cubic meters or grams per kilogram. “Relativehumidity”, for the purposes of this disclosure, is expressed as apercentage, indicating a present stat of absolute humidity relative to amaximum humidity given the same temperature. “Specific humidity”, forthe purposes of this disclosure, is the ratio of water vapor mass tototal moist air parcel mass, where parcel is a given portion of agaseous medium. Humidity sensor 304 may be psychrometer. Humidity sensor304 may be a hygrometer. Humidity sensor 304 may be configured to act asor include a humidistat. A “humidistat”, for the purposes of thisdisclosure, is a humidity-triggered switch, often used to controlanother electronic device. Humidity sensor 304 may use capacitance tomeasure relative humidity and include in itself, or as an externalcomponent, include a device to convert relative humidity measurements toabsolute humidity measurements. “Capacitance”, for the purposes of thisdisclosure, is the ability of a system to store an electric charge, inthis case the system is a parcel of air which may be near, adjacent to,or above a battery cell.

With continued reference to FIG. 3, sensor suite 300 may includemultimeter 308. Multimeter 308 may be configured to measure voltageacross a component, electrical current through a component, andresistance of a component. Multimeter 308 may include separate sensorsto measure each of the previously disclosed electrical characteristicssuch as voltmeter, ammeter, and ohmmeter, respectively.

Alternatively or additionally, and with continued reference to FIG. 3,sensor suite 300 may include a sensor or plurality thereof that maydetect voltage and direct the charging of individual battery cellsaccording to charge level; detection may be performed using any suitablecomponent, set of components, and/or mechanism for direct or indirectmeasurement and/or detection of voltage levels, including withoutlimitation comparators, analog to digital converters, any form ofvoltmeter, or the like. Sensor suite 300 may also include a sensor or aplurality thereof to detect voltage in order to determine which electricpower converters 116 to activate and deactivate. Sensor suite 300 and/ora control circuit incorporated therein and/or communicatively connectedthereto may be configured to adjust charge to one or more battery cellsas a function of a charge level and/or a detected parameter. Forinstance, and without limitation, sensor suite 300 may be configured todetermine that a charge level of a battery cell is high based on adetected voltage level of that battery cell or portion of the batterypack. Sensor suite 300 may alternatively or additionally detect a chargereduction event, defined for purposes of this disclosure as anytemporary or permanent state of a battery cell requiring reduction orcessation of charging; a charge reduction event may include a cell beingfully charged and/or a cell undergoing a physical and/or electricalprocess that makes continued charging at a current voltage and/orcurrent level inadvisable due to a risk that the cell will be damaged,will overheat, or the like. Detection of a charge reduction event mayinclude detection of a temperature, of the cell above a threshold level,detection of a voltage and/or resistance level above or below athreshold, or the like. Sensor suite 300 may include digital sensors,analog sensors, or a combination thereof. Sensor suite 300 may includedigital-to-analog converters (DAC), analog-to-digital converters (ADC,A/D, A-to-D), a combination thereof, or other signal conditioningcomponents used in transmission of a first plurality of battery packdata 216 to a destination over wireless or wired connection.

With continued reference to FIG. 3, sensor suite 300 may includethermocouples, thermistors, thermometers, passive infrared sensors,resistance temperature sensors (RTD's), semiconductor based integratedcircuits (IC), a combination thereof or another undisclosed sensor type,alone or in combination. Temperature, for the purposes of thisdisclosure, and as would be appreciated by someone of ordinary skill inthe art, is a measure of the heat energy of a system. Temperature, asmeasured by any number or combinations of sensors present within sensorsuite 300, may be measured in Fahrenheit (° F.), Celsius (° C.), Kelvin(° K), or another scale alone or in combination. The temperaturemeasured by sensors may comprise electrical signals which aretransmitted to their appropriate destination wireless or through a wiredconnection.

With continued reference to FIG. 3, sensor suite 300 may include asensor configured to detect gas that may be emitted during or after acatastrophic cell failure. “Catastrophic cell failure”, for the purposesof this disclosure, refers to a malfunction of a battery cell, which maybe an electrochemical cell, that renders the cell inoperable for itsdesigned function, namely providing electrical energy to at least aportion of an electric aircraft. Byproducts of catastrophic cell failuremay include gaseous discharge including oxygen, hydrogen, carbondioxide, methane, carbon monoxide, a combination thereof, or anotherundisclosed gas, alone or in combination. Further the sensor configuredto detect vent gas from electrochemical cells may comprise a gasdetector. For the purposes of this disclosure, a “gas detector” is adevice used to detect a gas is present in an area. Gas detectors, andmore specifically, the gas sensor that may be used in sensor suite 300,may be configured to detect combustible, flammable, toxic, oxygendepleted, a combination thereof, or another type of gas alone or incombination. The gas sensor that may be present in sensor suite 300 mayinclude a combustible gas, photoionization detectors, electrochemicalgas sensors, ultrasonic sensors, metal-oxide-semiconductor (MOS)sensors, infrared imaging sensors, a combination thereof, or anotherundisclosed type of gas sensor alone or in combination. Sensor suite 300may include sensors that are configured to detect non-gaseous byproductsof catastrophic cell failure including, in non-limiting examples, liquidchemical leaks including aqueous alkaline solution, ionomer, moltenphosphoric acid, liquid electrolytes with redox shuttle and ionomer, andsalt water, among others. Sensor suite 300 may include sensors that areconfigured to detect non-gaseous byproducts of catastrophic cell failureincluding, in non-limiting examples, electrical anomalies as detected byany of the previous disclosed sensors or components.

With continued reference to FIG. 3, sensor suite 300 may be configuredto detect events where voltage nears an upper voltage threshold or lowervoltage threshold. The upper voltage threshold may be stored in datastorage system 120 for comparison with an instant measurement taken byany combination of sensors present within sensor suite 300. The uppervoltage threshold may be calculated and calibrated based on factorsrelating to battery cell health, maintenance history, location withinbattery pack, designed application, and type, among others. Sensor suite300 may measure voltage at an instant, over a period of time, orperiodically. Sensor suite 300 may be configured to operate at any ofthese detection modes, switch between modes, or simultaneous measure inmore than one mode. Battery management component 132 may detect throughsensor suite 300 events where voltage nears the lower voltage threshold.The lower voltage threshold may indicate power loss to or from anindividual battery cell or portion of the battery pack. Batterymanagement component 132 may detect through sensor suite 300 eventswhere voltage exceeds the upper and lower voltage threshold. Eventswhere voltage exceeds the upper and lower voltage threshold may indicatebattery cell failure or electrical anomalies that could lead topotentially dangerous situations for aircraft and personnel that may bepresent in or near its operation.

FIG. 4, depicts a block diagram of a battery management and monitoringsystem 400. Battery management and monitoring system 400 includingbattery management component 404 and battery monitoring component 408.In some embodiments, battery management and monitoring system 400 mayalso include interlock component 412. Battery management and monitoringsystem 400 is disposed on at least a portion of battery pack 224. Forexample, battery pack 224 may include batteries, includingelectrochemical battery cells, consistent with the descriptionhereinabove. Battery management and monitoring system 400 may includemore than one electrically isolated systems performing at least aportion of the same functions. Battery management and monitoring system400 may include more than one electrically isolated systems performingredundant functions. Battery management and monitoring system 400 mayinclude more than one electrically isolated systems performing entirelydifferent functions. Battery management and monitoring system 400 mayinclude more than one electrically isolated systems performing entirelyseparate and distinct functions. Battery management and monitoringsystem 400 may include one or more physically separated systems disposedon at least a distinct portion of battery pack 224 or any subcomponentsthereof. Battery management and monitoring system 400 may include one ormore physically isolated systems that perform at least a portion of thesame functions. Battery management and monitoring system 400 may includemore than one physically isolated systems performing the redundantfunctions. Battery management and monitoring system 400 may include morethan one physically isolated systems performing entirely differentfunctions. Battery management and monitoring system 400 may include morethan one physically isolated systems performing entirely separate anddistinct functions.

With continued reference to FIG. 4, battery management and monitoringsystem 400 may include a sensor suite 416. The sensor suite may includeany sensor suite described above consistent with the disclosure, forexample, first sensor suite 212 or second sensor suite 228. Sensor suite416 is configured to measure a plurality of battery pack data 420.Plurality of battery pack data 420 may include any plurality of batterypack data described above with reference to FIG. 2, namely firstplurality of battery pack data 216 and second plurality of battery packdata 232. Sensor suite 416 may include any of the sensors, grouping ofsensors, or prefabricated sensor packages as described above. Sensorsuite 416 may include an accelerometer. Sensor suite 416 may include avibrometer, vibration sensor, load cell, pressure sensor, force gauge, acombination thereof, among other sensors configured to measure physicalparameters like acceleration, force, vibration, pressure, and the like.Sensor suite 416 may include a voltmeter. Additionally, sensor suite 416may include a multimeter, configured to measure electrical current,potential difference (voltage), resistance, impedance, capacitance, orother electrical parameters alone or in combination. Sensor suite 416may include an ohmmeter, ammeter, or other separate electrical sensors.Sensor suite 416 may include a thermocouple. Additionally oralternatively, sensor suite 416 may include a thermometer, RTD, or othersensor configured to measure temperature or heat energy of a system.

With continued reference to FIG. 4, battery management and monitoringsystem 400 includes a battery monitoring component 408. Batterymonitoring component 408 is configured to measure, as a function ofplurality of battery pack data 420, first fault 424 in battery pack 224.In an embodiment, battery monitoring component 408 may use a sensorsuite 416 to measure a plurality of battery pack data 420. Batterymonitoring component 408 may be disposed on at least a portion of anintegrated circuit board on or in battery pack 224. The integratedcircuit board may be disposed in battery pack 224 proximate to batterycells or disposed on a first end of battery pack 224. First fault 424may include an over-voltage condition of at least a portion of batterypack 224, for example, a single electrochemical battery cellover-voltage, or a portion thereof. First fault 424 may include anunder-voltage condition of at least a portion of battery pack 224. Firstfault 424 may be characterized by a comparison, by battery monitoringcomponent 408, of a voltage measurement from sensor suite 416, to avoltage threshold which has been predetermined or calculated by at leasta user or additional system, or alternatively, input by a user. Firstfault 424 may include a temperature rise rate. There may be a thresholdtemperature rise rate or threshold temperature to which a temperaturemeasurement by sensor suite 416 is compared by battery monitoringcomponent 408. First fault 424 may include a detection of a resistance.This resistance may be measured by sensor suite 416 and compared to arange or threshold resistance input by a user, calculated by at least aportion of an alternate system, or a combination thereof. First fault424 may also include a detection that the loading of electric powerconverter(s) 116 exceeds upper current threshold 516. First fault 424may also include a detection that the loading of electric powerconverter(s) 116 falls below lower current threshold 512.

With continued reference to FIG. 4, battery monitoring component 408produces a first fault detection response 428 upon detection of firstfault 424. First fault detection response 428 may be generated inresponse to any of the described variations of first fault 424. This isa non-exhaustive list of possible faults that may be detected as firstfault 424, one of ordinary skill in the art would understand the greaternumber and variation of physical, electrical, or other faults that maybe detected by a sensor suite configured to measure characteristics ofan electric aircraft battery pack. First fault detection response 428includes notification of a user of the first fault 424 in battery pack224. Battery monitoring component 408 communicates first fault detectionresponse 428 to be displayed on graphical user interface 432. Graphicaluser interface (GUI) 432 may include a flight display known in the artto be disposed in at least a portion of a cockpit of an electricaircraft. GUI 432 may be disposed on a user device located remotely fromthe electric aircraft. GUI 432 may be disposed on a computer devicelocated remotely or onboard the electric aircraft. GUI 432 may bedisposed on a smartphone located remotely or onboard the electricaircraft. First fault detection response 428 may include a textualdisplay. The textual display may include a warning message to a user,which may include a pilot, whether onboard or remotely located. Thetextual display may include a message describing the fault.Additionally, or alternatively, the textual display my include a genericmessage that a fault was detected. The textual display may include wherethe fault was located within battery pack 224. The textual display mayinclude a suggestion for pilot or user intervention or suggestedmaintenance procedures. First fault detection response 428 may includean image display. The image display included in first fault detectionresponse 428 may include a depiction of battery pack 224. The imagedisplay may include a depiction of a portion of battery pack 224. Theimage display may include a depiction of the portion of battery pack 224first fault 424 was detected in. The image display may include adepiction of suggested user operations or suggested maintenance. Itshould be noted that battery monitoring component 408 is only capable ofnotifying a user of first fault 424 by first fault detection response428.

With continued reference to FIG. 4, battery management and monitoringsystem 400 includes battery management component 404. Battery managementcomponent may be consistent with the description of the batterymanagement components hereinabove, for instance, first and secondbattery management sub-components (204 and 208). Battery managementcomponent 404 is configured to detect, as a function of plurality ofbattery pack data 420, second fault 436 in battery pack 224. Secondfault 436 may be characterized exactly like first fault 424. Forexample, second fault 436 may include an over-voltage condition ortemperature rise rate. Second fault 436 may not be characterized likefirst fault 424. For example, second fault 436 may be an over-voltagecondition and first fault 424 may be an undervoltage condition. Firstfault 424 and second fault 436 may be detected separately from eachother, at least partially together, or at the same instant. One ofordinary skill in the art would understand first fault 424 and secondfault 436 to have near limitless combinations and/or iterations thereof.First fault 424 does not necessarily need to be detected before secondfault 436 chronologically, and largely depends on the active componentat the time, which will be described in detail herein. Batterymanagement component 404 is configured to produce a second faultdetection response 440 upon receiving detection of second fault 436.Second fault detection response 440 is configured to mitigate secondfault 436 in battery pack 224. “Mitigate”, for the purposes of thisdisclosure, describes operations, procedures, actions, orreconfigurations with the intent to resolve an operational fault in acomponent of a system. In a non-limiting example, battery managementcomponent 404 may redirect current around at least a portion of batterypack 224 if second fault 436 is detected in at least a portion ofbattery pack 224. The mitigation would be to bypass the malfunctioningarea of the battery pack, in this non-limiting example. Second faultdetection response 440 may additionally include a prioritization ofcurrent to a portion of battery pack 224 that is experiencing a lack ofcharging to that portion, thus mitigating the charging difference withinbattery pack 224. Battery management component 404 may include acontactor control circuit. “Contactor control circuit”, for the purposesof this disclosure, describes an electrically controlled switch used forswitching an electrical power circuit, here found in battery pack 224.Typically, a contactor control circuit is controlled by a circuit whichhas a lower power level than the switched circuit. In some embodiments,second fault detection response may include disabling an electric powerconverter, for instance, electric power converter 116 with reference toFIG. 1. One of ordinary skill in the art would understand that there area plurality of methods and systems capable of switching circuitselectromechanically, like relays, and that a plurality may be usedherein substituted for contactor control circuit.

With continued reference to FIG. 4, battery management and monitoringsystem 400 may include interlock component 412. Interlock component 412includes a first mode 444 and a second mode 448. Interlock component 412is configured to enable battery monitoring component 408 and disablebattery management component 404 when in first mode 444. Interlockcomponent is configured to enable battery management component 404 anddisable battery monitoring component 408 when in second mode 448. One ofordinary skill in the art would understand that first mode and secondmode do not refer to order of operations or chronology, but to more thanone distinct mode the interlock component 412 can reconfigure itselfinto. One of ordinary skill in the art would appreciate from the presentdisclosure that battery management component 404 and battery monitoringcomponent 408 are enabled and disabled separately. In other words, theenabling of one component does not disable the other automatedly, forexample. Interlock component 412 may include a mechanical component. Forexample, a mechanical interlock component may include a lever, button,switch that is physically interacted with by a user, subsystem, or acombination thereof. Interlock component 412 may include an electricalcomponent. For example, an electrical interlock component 412 mayinclude a circuit that is completed when a certain component is to beenabled. Interlock component 412 may enable battery monitoring component408 when battery pack 224 is installed in electric aircraft. In thisnon-limiting example, a mechanical and/or electrical interlock componentdisposed in or on battery pack 224 may be actuated when battery pack isinstalled in electric aircraft. Specifically, and in a non-limitingembodiment, a latching system used to secure battery pack 224 is engagedaround a portion of battery pack 224, the latching system may actuate amechanical interlock component, or complete the circuit of an electricalinterlock component to thus enable battery monitoring component 408.Additionally, or alternatively, when installed in electric aircraft,interlock component 412 may enter first mode 444, enabling batterymonitoring component 408 and disabling battery management component 404.In another non-limiting example, interlock component 412 enters secondmode 448 and thus enables battery management component 404 when thebattery is uninstalled from the electric aircraft. Interlock component412 may enter second mode 448, enabling battery management component 404during charging of battery pack 224. Interlock component 412 may entersecond mode 448, enabling battery management component 404 duringtesting of battery pack 224. In a non-limiting embodiment, batterymonitoring component 408 is enabled by interlock component 412 whenbattery pack is installed in electric aircraft, and, thus, when electricaircraft is in flight mode. In a non-limiting embodiment, interlockcomponent 412 may be a combinatorial and/or sequential logic circuit. Inanother non-limiting embodiment, interlock component 412 may include afinite state machine. In some cases, interlock component 412 may includean analog circuit or a processor. The processor may include anyprocessor described in this disclosure. It would follow to one ofordinary skill in the art, upon reviewing the entirety of thisdisclosure, that when battery pack 224 is uninstalled from electricaircraft, battery management component 404 is enabled when battery packis offboard of electric aircraft. In other words, faults detected inflight can only be detected and displayed to a user, wherein thediscretion of the user is used to mitigate faults, as opposed tooffboard electric aircraft when battery management system can mitigaterisks without user intervention, in a non-limiting example.

With continued reference to FIG. 4, battery management and monitoringsystem may include battery management system head unit (BMSHU) 452configured to electronically communicate with a controller. BMSHU 452may be consistent with any communicatively coupled electronic componentdescribed in this disclosure. The controller may be any circuit,computing device, or combination of electronics and power electronicsconsistent with this disclosure.

FIG. 5 is a graph of the efficiency of an electric power converter 116plotted against the loading (output current as a percentage of outputcurrent). FIG. 5 shows that the electric power converter 116 has adesirable operating region 504 at which point electric power converter116 is at or near its peak efficiency. In this case, in someembodiments, “near” may be within 10% of the electric power converter's116 peak efficiency. In other embodiments, “near” may be within 20% ofthe electric power converter's 116 peak efficiency. Alternatively, insome embodiments, desirable operating region 504 may be defined bythreshold efficiency 508. In this case, loadings where electric powerconverter 116 functions above threshold efficiency 508 are part of thedesirable operating region 504. Desirable operating region is bounded onthe lower end by a lower current threshold 512 and on the upper end byan upper current threshold 516. When the loading of electric powerconverter 116 falls below lower current threshold 512, electric powerconverter controller 124 may disable an electric power converter 116such that the loading falls within desirable operating region 504. Insome embodiments, electric power converter controller 124 may disablemore than one electric power converter 116 such that the loading fallswithin desirable operating region 504. When the loading of electricpower converter 116 exceeds upper current threshold 516, electric powerconverter controller 124 may enable an electric power converter 116 suchthat the loading falls within desirable operating region 504. In someembodiments, electric power converter controller 124 may enable morethan one electric power converter 116 such that the loading falls withindesirable operating region 504.

FIG. 6 is a flowchart of method for aircraft power management anddistribution 600. Method 600 includes a step 604 of measuring batterpack data for a plurality of batteries in a battery pack. Battery packdata can be any battery pack data described in this disclosure. Theplurality of batteries can be any plurality of batteries described inthis disclosure. Method 600 also includes step 608 of selecting, from aplurality of electric power converters, wherein each electric powerconverter in the plurality of electric power converters is connected toa battery of the plurality of batteries, one or more electric powerconverters to enable. Electric power converters can be any electricpower converters in this disclosure. Step 612 entails enabling the oneof more selected electric power converters and includes at least twosub-steps, including step 616 and step 620. Step 616 includesconverting, using an electric power converter of the one or moreselected electric power converters, an input at a first voltage level toan output at a second voltage level. Step 620 includes combining eachoutput of the one or more enabled electric power converters to create atotal output. Method 600 also includes step 624 of comparing the totaloutput of the one or more enabled electric power converters against anoptimal operating region, having a first threshold value and a secondthreshold value. optimal operating region can be any optimal operatingregion disclosed in this disclosure. first and second threshold valuecan be any first and second threshold value disclosed in thisdisclosure. Method 600 also includes step 628 of adjusting, when thetotal output of the one or more enabled electric power converters isoutside of the optimal operating region, a number of the one or moreenabled electric power converters.

In some non-limiting embodiments, method 600 may also include a step ofadjusting an interlock component, wherein the interlock component has afirst mode and second mode. Interlock component may be consistent withany interlock component disclosed in this disclosure. This step may, insome embodiments, include the further sub-steps of enabling the batterymonitoring component and disabling the battery management component whenin the first mode and enabling the battery management component anddisabling the battery monitoring component when in the second mode.

In some non-limiting embodiments, method 600 may also include a step ofenabling the battery monitoring component when the battery pack isinstalled in an electric aircraft. In some non-limiting embodiments,method 600 may also include a step of enabling the battery managementcomponent when the battery pack is uninstalled from the electricaircraft. In some non-limiting embodiments of method 600, step 628 mayfurther include a sub-step of increasing the number of the one or moreenabled electric power converters when the total output of the one ormore enabled electric power converters exceeds the second thresholdvalue. In some non-limiting embodiments of method 600, step 628 mayfurther include a sub-step of decreasing the number of the one or moreenabled electric power converters when the total output of the one ormore enabled electric power converters does not exceed the firstthreshold value. First threshold value and second threshold value may beconsistent with any first threshold value and second threshold valuedisclosed as part of this disclosure.

In some non-limiting embodiments, method 600 may further include a stepof estimating a charge state of each of the plurality of batteries basedon a current output of the respective electric power converter. In somenon-limiting embodiments, method 600 may further include a step ofdetermining an amount of charge withdrawn from the plurality ofbatteries based on the current output of the respective electric powerconverter. In some non-limiting embodiments, method 600 may furtherinclude a step of determining an amount of time that each of theplurality of electric power converters is enabled. In some non-limitingembodiments, method 600 may further include a step of rotating throughthe plurality of electric power converters so that each of the electricpower converters is enabled for approximately an equal length of time.In some non-limiting embodiments, method 600 may further include thesteps of detecting, as a function of the plurality of battery pack data,a first fault in the battery pack; and detecting, as a function of theplurality of battery pack data, a second fault in the battery pack. Insome non-limiting embodiments, method 600 may further include a step ofproducing a first fault detection response notifying a user of the firstfault in the battery pack. The first fault detection response may beconsistent with any first fault detection response disclosed in thisdisclosure. In some non-limiting embodiments, method 600 may furtherinclude a step of producing a second fault detection response, whereinthe second fault detection response is configured to mitigate the secondfault in the battery pack. Second fault detection response may beconsistent with any second fault detection response disclosed in thisdisclosure.

Now, referring to FIG. 7, in some embodiments, controller 112 may beincorporated into a flight controller. Alternatively, some of thecomponents of controller 112 may be incorporated into a flightcontroller. An exemplary embodiment 700 of a flight controller 704 isillustrated. As used in this disclosure a “flight controller” is acomputing device of a plurality of computing devices dedicated to datastorage, security, distribution of traffic for load balancing, andflight instruction. Flight controller 704 may include and/or communicatewith any computing device as described in this disclosure, includingwithout limitation a microcontroller, microprocessor, digital signalprocessor (DSP) and/or system on a chip (SoC) as described in thisdisclosure. Further, flight controller 704 may include a singlecomputing device operating independently, or may include two or morecomputing device operating in concert, in parallel, sequentially or thelike; two or more computing devices may be included together in a singlecomputing device or in two or more computing devices. In embodiments,flight controller 704 may be installed in an aircraft, may control theaircraft remotely, and/or may include an element installed in theaircraft and a remote element in communication therewith.

In an embodiment, and still referring to FIG. 7, flight controller 704may include a signal transformation component 708. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 708 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component708 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 708 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 708 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 708 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 7, signal transformation component 708 may beconfigured to optimize an intermediate representation 712. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 708 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 708 may optimizeintermediate representation 712 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 708 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 708 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 704. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 708 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 7, flight controller 704may include a reconfigurable hardware platform 716. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 716 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 7, reconfigurable hardware platform 716 mayinclude a logic component 720. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 720 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 720 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 720 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 720 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 720 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 712. Logiccomponent 720 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 704. Logiccomponent 720 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 720 may beconfigured to execute the instruction on intermediate representation 712and/or output language. For example, and without limitation, logiccomponent 720 may be configured to execute an addition operation onintermediate representation 712 and/or output language.

In an embodiment, and without limitation, logic component 720 may beconfigured to calculate a flight element 724. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 724 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 724 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 724 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 7, flight controller 704 may include a chipsetcomponent 728. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 728 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 720 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 728 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 720 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 728 maymanage data flow between logic component 720, memory cache, and a flightcomponent 732. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 732 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component732 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 728 may be configured to communicate witha plurality of flight components as a function of flight element 724.For example, and without limitation, chipset component 728 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 7, flight controller 704may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 704 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 724. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 704 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 704 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 7, flight controller 704may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 724 and a pilot signal736 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 736may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 736 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 736may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 736 may include an explicitsignal directing flight controller 704 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 736 may include an implicit signal, wherein flight controller 704detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 736 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 736 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 736 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 736 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal736 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 7, autonomous machine-learning model may includeone or more autonomous machine-learning processes such as supervised,unsupervised, or reinforcement machine-learning processes that flightcontroller 704 and/or a remote device may or may not use in thegeneration of autonomous function. As used in this disclosure “remotedevice” is an external device to flight controller 704. Additionally oralternatively, autonomous machine-learning model may include one or moreautonomous machine-learning processes that a field-programmable gatearray (FPGA) may or may not use in the generation of autonomousfunction. Autonomous machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elasticnet regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naïve bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 7, autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 704 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 7, flight controller 704 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 704. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 704 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 704 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 7, flight controller 704 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 7, flight controller 704may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller704 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 704 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 704 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 7, control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 732. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 7, the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 704. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 712 and/or output language from logiccomponent 720, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 7, master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 7, control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 7, flight controller 704 may also be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of aircraft and/orcomputing device. Flight controller 704 may include a distributer flightcontroller. As used in this disclosure a “distributer flight controller”is a component that adjusts and/or controls a plurality of flightcomponents as a function of a plurality of flight controllers. Forexample, distributer flight controller may include a flight controllerthat communicates with a plurality of additional flight controllersand/or clusters of flight controllers. In an embodiment, distributedflight control may include one or more neural networks. For example,neural network also known as an artificial neural network, is a networkof “nodes,” or data structures having one or more inputs, one or moreoutputs, and a function determining outputs based on inputs. Such nodesmay be organized in a network, such as without limitation aconvolutional neural network, including an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 7, a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 7, flight controller may include asub-controller 740. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 704 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 740may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 740 may include any component of any flightcontroller as described above. Sub-controller 740 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 740may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 740 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 7, flight controller may include a co-controller744. As used in this disclosure a “co-controller” is a controller and/orcomponent that joins flight controller 704 as components and/or nodes ofa distributer flight controller as described above. For example, andwithout limitation, co-controller 744 may include one or morecontrollers and/or components that are similar to flight controller 704.As a further non-limiting example, co-controller 744 may include anycontroller and/or component that joins flight controller 704 todistributer flight controller. As a further non-limiting example,co-controller 744 may include one or more processors, logic componentsand/or computing devices capable of receiving, processing, and/ortransmitting data to and/or from flight controller 704 to distributedflight control system. Co-controller 744 may include any component ofany flight controller as described above. Co-controller 744 may beimplemented in any manner suitable for implementation of a flightcontroller as described above.

In an embodiment, and with continued reference to FIG. 7, flightcontroller 704 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 704 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 8, an exemplary embodiment of a machine-learningmodule 800 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 804 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 808 given data provided as inputs 812;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 8, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 804 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 804 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 804 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 804 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 804 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 804 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data804 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 8,training data 804 may include one or more elements that are notcategorized; that is, training data 804 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 804 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 804 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 804 used by machine-learning module 800 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 8, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 816. Training data classifier 816 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 800 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 804. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 8, machine-learning module 800 may be configuredto perform a lazy-learning process 820 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 804. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 804elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Alternatively or additionally, and with continued reference to FIG. 8,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 824. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 824 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 824 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 804set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 8, machine-learning algorithms may include atleast a supervised machine-learning process 828. At least a supervisedmachine-learning process 828, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 804. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process828 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 8, machine learning processes may include atleast an unsupervised machine-learning processes 832. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 8, machine-learning module 800 may be designedand configured to create a machine-learning model 824 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 8, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes

FIG. 9 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 900 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 900 includes a processor 904 and a memory908 that communicate with each other, and with other components, via abus 912. Bus 912 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 904 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 904 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 904 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 908 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 916 (BIOS), including basic routines that help totransfer information between elements within computer system 900, suchas during start-up, may be stored in memory 908. Memory 908 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 920 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 908 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 900 may also include a storage device 924. Examples of astorage device (e.g., storage device 924) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 924 may be connected to bus 912 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 924 (or one or morecomponents thereof) may be removably interfaced with computer system 900(e.g., via an external port connector (not shown)). Particularly,storage device 924 and an associated machine-readable medium 928 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 900. In one example, software 920 may reside, completelyor partially, within machine-readable medium 928. In another example,software 920 may reside, completely or partially, within processor 904.

Computer system 900 may also include an input device 932. In oneexample, a user of computer system 900 may enter commands and/or otherinformation into computer system 900 via input device 932. Examples ofan input device 932 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 932may be interfaced to bus 912 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 912, and any combinations thereof. Input device 932 mayinclude a touch screen interface that may be a part of or separate fromdisplay 936, discussed further below. Input device 932 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 900 via storage device 924 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 940. A network interfacedevice, such as network interface device 940, may be utilized forconnecting computer system 900 to one or more of a variety of networks,such as network 944, and one or more remote devices 948 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 944,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 920,etc.) may be communicated to and/or from computer system 900 via networkinterface device 940.

Computer system 900 may further include a video display adapter 952 forcommunicating a displayable image to a display device, such as displaydevice 936. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 952 and display device 936 may be utilized incombination with processor 904 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 900 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 912 via a peripheral interface 956. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods andsystems according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for aircraft power management anddistribution, comprising: a battery pack, the battery pack comprising: aplurality of batteries electrically connected together in a series; abattery monitoring component, the battery monitoring componentconfigured to measure battery pack data; a battery management component;an interlock component, having a first mode and a second mode, theinterlock component configured to: enable the battery monitoringcomponent and disable the battery management component when in the firstmode when installed in an electric aircraft; and enable the batterymanagement component and disable the battery monitoring component whenin the second mode; enable the battery monitoring component when thebattery pack is installed in an electric aircraft; enable the batterymanagement component when the battery pack is uninstalled from theelectric aircraft; and a plurality of electric power converters, whereineach electric power converter of the plurality of electric powerconverters is connected to a battery in the plurality of batteries; anelectric power converter controller, the electric power convertercontroller configured to: control each electric power converter of aplurality of electric power converters; select, from the plurality ofelectric power converters, one or more electric power converters toenable; enable the one or more selected electric power converters,wherein: an enabled electric power converter converts an input at afirst voltage level to an output at a second voltage level; and theoutputs of the one or more enabled electric power converters arecombined to create a total output; compare the total output of the oneor more enabled electric power converters against an optimal operatingregion, having a first threshold value and a second threshold value; andadjust, when the total output of the one or more enabled electric powerconverters is outside of the optimal operating region, a number of theone or more enabled electric power converters, wherein adjusting furthercomprises adjusting as a function of the battery pack data.
 2. Thesystem for aircraft power management and distribution of claim 1,wherein adjusting the number of the one or more enabled electric powerconverters comprises: increasing the number of the one or more enabledelectric power converters when the total output of the one or moreenabled electric power converters exceeds the second threshold value. 3.The system for aircraft power management and distribution of claim 1,wherein adjusting the number of the one or more enabled electric powerconverters comprises: decreasing the number of the one or more enabledelectric power converters when the total output of the one or moreenabled electric power converters does not exceed the first thresholdvalue.
 4. The system for aircraft power management and distribution ofclaim 1, wherein the electric power converter controller is furtherconfigured to: estimate a charge state of each of the plurality ofbatteries based on a current output of the respective electric powerconverter; and determine an amount of charge withdrawn from theplurality of batteries based on the current output of the respectiveelectric power converter.
 5. The system for aircraft power managementand distribution of claim 1, wherein the electric power convertercontroller is further configured to: determine an amount of time thateach of the plurality of electric power converters is enabled; androtate through the plurality of electric power converters so that eachof the electric power converters is enabled for approximately an equallength of time.
 6. The system for aircraft power management anddistribution of claim 5, wherein the electric power converter controlleruses a machine learning algorithm.
 7. The system for aircraft powermanagement and distribution of claim 1, further comprising a pluralityof flight components, the plurality of flight components receiving thetotal output.
 8. The system for aircraft power management anddistribution of claim 1, wherein: the battery monitoring component isconfigured to: detect a first fault in the battery pack based on thebattery pack data; and produce a first fault detection responsenotifying a user of the first fault in the battery pack; and the batterymanagement component is configured to: detect a second fault in thebattery pack based on the battery pack data; and produce a second faultdetection response, wherein the second fault detection response isconfigured to mitigate the second fault in the battery pack.
 9. A methodfor aircraft power management and distribution, comprising: measuring,at a battery monitoring component and a battery management component,battery pack data for a plurality of batteries in a battery pack;selecting, from a plurality of electric power converters, wherein eachelectric power converter in the plurality of electric power convertersis connected to a battery of the plurality of batteries, one or moreelectric power converters to enable; enabling the one of more selectedelectric power converters, comprising: converting, using an electricpower converter of the one or more selected electric power converters,an input at a first voltage level to an output at a second voltagelevel; and combining each output of the one or more enabled electricpower converters to create a total output; comparing the total output ofthe one or more enabled electric power converters against an optimaloperating region, having a first threshold value and a second thresholdvalue; adjusting, when the total output of the one or more enabledelectric power converters is outside of the optimal operating region, anumber of the one or more enabled electric power converters; andadjusting an interlock component, the interlock component having a firstmode and a second mode, comprising: enabling the battery monitoringcomponent and disabling the battery management component when in thefirst mode; enabling the battery management component and disabling thebattery monitoring component when in the second mode; enabling thebattery monitoring component when the battery pack is installed in anelectric aircraft; and enabling the battery management component whenthe battery pack is uninstalled from the electric aircraft.
 10. Themethod for aircraft power management and distribution of claim 9,wherein adjusting the number of the one or more enabled electric powerconverters comprises increasing the number of the one or more enabledelectric power converters when the total output of the one or moreenabled electric power converters exceeds the second threshold value.11. The method for aircraft power management and distribution of claim9, wherein adjusting the number of the one or more enabled electricpower converters comprises decreasing the number of the one or moreenabled electric power converters when the total output of the one ormore enabled electric power converters does not exceed the firstthreshold value.
 12. The method for aircraft power management anddistribution of claim 9, further comprising: estimating a charge stateof each of the plurality of batteries based on a current output of therespective electric power converter; and determining an amount of chargewithdrawn from the plurality of batteries based on the current output ofthe respective electric power converter.
 13. The method for aircraftpower management and distribution of claim 12, further comprising:determining an amount of time that each of the plurality of electricpower converters is enabled; and rotating through the plurality ofelectric power converters so that each of the electric power convertersis enabled for approximately an equal length of time.
 14. The method foraircraft power management and distribution of claim 9, furthercomprising: detecting, as a function of the plurality of battery packdata, a first fault in the battery pack; and detecting, as a function ofthe plurality of battery pack data, a second fault in the battery pack.15. The method for aircraft power management and distribution of claim14, further comprising: producing a first fault detection responsenotifying a user of the first fault in the battery pack; and producing asecond fault detection response, wherein the second fault detectionresponse is configured to mitigate the second fault in the battery pack.